AI Model Comparison 2026
Open source and proprietary frontier models compared across architecture, cost, performance, and on-premises deployment suitability. Updated October 2026.
Key takeaways
- Claude Opus 5.5 holds the top Artificial Analysis Intelligence Index v4.3.x score at 58 (max effort) and lists at $4 input and $20 output per 1M tokens.
- The best open-weight model, Xiaomi’s MiMo-V2.6-Pro, scores 46 on Artificial Analysis v4.3.x at about $0.13 per task, versus $1.99–$7.63 for the 53-point GPT-6 Astra, Fable 5.1 and Gemini 4 Argon tier.
- Flagship API prices keep rising, with GPT-6 Astra and Claude Fable 5.1 at $10 / $50 per 1M tokens, while mid-tier prices fell, such as OpenAI’s GPT-6.1 Sol at $2 / $10.
- At 4-bit quantization, Qwen3.8-27B runs on the 128GB Starter Mac Studio, and GLM-5.3-Flash, MiMo-V2.6-Flash and, as a tighter fit, MiniMax M3 on the 512GB Scale Mac Studio; DeepSeek V4.1 Flash runs on Scale as a 1- to 3-bit build and needs a larger setup at 4-bit.
- Faraday Machines installs open-weight models on Mac Studio hardware on-premises, from the $299 US a month Starter tier up to larger setups for GLM-5.3, MiMo-V2.6-Pro and Kimi K3.
At a Glance
| Model | Type | Architecture | Context | Input / 1M | Output / 1M | AA Index v4.3 | Faraday Fit | Best For |
|---|---|---|---|---|---|---|---|---|
| MiMo-V2.6-Pro | Open | MoE, 1.02T / 42B active | 1M | — | — | 46 | Larger setup (talk to us) | Highest-scoring open model |
| GLM-5.3 | Open | MoE, ~753B / ~40B active | 1M | $1.40 | $4.40 | 45 (max) | Larger setup; Scale at 1–2-bit | Agentic coding, enterprise |
| Kimi K3 | Open | MoE, 2.8T / 104B active | 1M | $3.00 | $15.00 | 44 (max) | Larger setup (also at 1-bit) | Frontend code, browsing agents |
| GLM-5.3-Flash | Open | MoE, 320B / 18B active | 1M | — | — | 42 | Scale (1 × Mac Studio, 512 GB); Growth at 1–2-bit | Business intelligence, documents, multimodal |
| MiMo-V2.6-Flash | Open | MoE, 309B / 15B active | — | — | — | 38 | Scale (1 × Mac Studio, 512 GB) | Cost-efficient generalist |
| DeepSeek V4.1 Flash | Open | MoE, 552B / 16B active | 1M | $0.30 | $1.20 | — | Scale (1 × Mac Studio, 512 GB) at 1–3-bit; larger setup at 4-bit | Agentic coding, long context |
| MiniMax M3 | Open | MoE, ~428B / ~23B active, multimodal | 1M | $0.60 | $2.40 | — | Scale (1 × Mac Studio, 512 GB), a tighter fit | Long-context, multimodal agents |
| Qwen3.8-27B | Open | Dense 27B, vision-language | 262K | — | — | — | Starter (1 × Mac Studio, 128 GB) | Fast coding, multilingual, image & video |
| Claude Opus 5.5 | Proprietary | Undisclosed | — | $4.00 | $20.00 | 58 (max) | Cloud API only | Hardest reasoning and coding |
| GPT-6 Astra | Proprietary | Undisclosed | — | $10.00 | $50.00 | 53 | Cloud API only | Frontier general purpose |
| Claude Fable 5.1 | Proprietary | Undisclosed | — | $10.00 | $50.00 | 53 | Cloud API only | Frontier general purpose |
| Gemini 4 Argon | Proprietary | Undisclosed | — | $2.00* | $10.00* | 53 | Cloud API only | Frontier scores at a launch-promo price |
Pricing reflects vendor list API rates per 1M tokens as of October 2026; DeepSeek V4.1 Flash is shown at its peak rate (off-peak is $0.15 / $0.60). *Gemini 4 Argon is shown at Google’s promotional 50%-off launch price (roughly $4 / $20 standard) and is still rolling out to selected users. “AA Index v4.3” is the Artificial Analysis Intelligence Index v4.3.x, which was rescaled in September 2026, so these scores are not comparable with the v4.1 scores in our earlier articles; “—” means we have no reliable v4.3 score or price. Faraday fit assumes 4-bit quantization (roughly 0.55 bytes per parameter) unless a lower bit width is shown. That rule does not hold for DeepSeek V4.1 Flash, whose mostly FP8 weights and ~196B-parameter Engram memory put its 4-bit builds at about 400–445 GB. The 1- to 3-bit fits use lower-bit builds, mostly Unsloth’s, which trade some quality, most noticeably on long or precise work. Each tier is one Mac Studio: Starter has 128 GB (M5 Max), Growth 256 GB and Scale 512 GB (M5 Ultra). Kimi K3, GLM-5.3 at 4-bit and MiMo-V2.6-Pro need larger setups (talk to us). Proprietary models are cloud-only: there are no weights to self-host.
Choosing for On-Premises Deployment
Open Source Advantage
With open weights you own the model, control the inference stack, and pay no per-token API fees. Licensing now varies more than it used to. DeepSeek V4.1 Flash, GLM-5.3-Flash and MiMo-V2.6 ship under the MIT license and Qwen3.8-27B under Apache 2.0. GLM-5.3 moved from MIT to a bespoke license, and Kimi K3 uses a custom license with thresholds for very large commercial deployments. Check the license before you standardize.
Proprietary Performance
Claude Opus 5.5 tops the Artificial Analysis Intelligence Index v4.3.x at 58 (max effort). The best open model, MiMo-V2.6-Pro, scores 46: the gap to the very top widened on v4.3, but so did the cost advantage, at about $0.13 per task against $1.99–$7.63 for the GPT-6 Astra, Fable 5.1 and Gemini 4 Argon tier (53). Flagship prices keep rising (GPT-6 Astra and Fable 5.1 cost $10 / $50), yet prices also fall without warning: Opus 5.5 cut 20% to $4 / $20, and Gemini 4 Argon launched at a promotional rate that will expire.
Context Windows
DeepSeek V4.1 Flash, GLM-5.3, GLM-5.3-Flash, Kimi K3 and MiMo-V2.6-Pro all reach 1M tokens. MiniMax M3 goes up to 1M (512K guaranteed), and its API bills long-context requests at 2×. Qwen3.8-27B handles 262K. On-premises deployment means no long-context surcharges: a million-token prompt costs the same as a short one.
Hardware Fit
At 4-bit quantization, Qwen3.8-27B (~17 GB) runs on the 128GB Mac Studio (Starter). MiMo-V2.6-Flash (~170 GB) and GLM-5.3-Flash (~180 GB) fit the 512GB Mac Studio (Scale), and GLM-5.3-Flash also fits the 256GB Growth tier as a 1- to 2-bit build (about 93–109 GB). MiniMax M3 (~240 GB) fits Scale too, as a tighter fit. DeepSeek V4.1 Flash is larger than its 552B backbone suggests, because it also carries a ~196B-parameter Engram memory: about 400–445 GB at 4-bit, so it needs a larger setup at 4-bit and runs on Scale as a 1- to 3-bit build (about 250–350 GB). GLM-5.3 (~410 GB), MiMo-V2.6-Pro (~560 GB) and Kimi K3 (~1.5 TB) need larger setups at 4-bit; Moonshot recommends supernodes of 64+ accelerators for K3. Unsloth’s low-bit builds change one of those: GLM-5.3 at 1–2-bit (about 223–239 GB) fits Scale, while Kimi K3 at 1-bit (594 GB) still needs a larger setup. Lower-bit builds trade some quality, most noticeably on long or precise work.
Frequently Asked Questions
Which AI model is the smartest in October 2026?
Claude Opus 5.5 holds the top score on the Artificial Analysis Intelligence Index v4.3.x, at 58 at max effort. GPT-6 Astra, Claude Fable 5.1 and Gemini 4 Argon follow at 53. The best open-weight model, Xiaomi’s MiMo-V2.6-Pro, scores 46. Because Artificial Analysis rescaled the index with v4.3 in September 2026, these scores can’t be compared with earlier versions.
How much does GPT-6 Astra cost compared with Claude Opus 5.5?
GPT-6 Astra lists at $10 per 1M input tokens and $50 per 1M output tokens, the same as Claude Fable 5.1. Claude Opus 5.5 lists at $4 input and $20 output, 20% below Claude Opus 5. Below the flagship tier, prices fell: OpenAI’s GPT-6.1 Sol costs $2 / $10. Open-weight models running on-premises, such as on Faraday Machines hardware, carry no per-token charge.
Can I run Claude or GPT models on-premises?
No. Claude Opus 5.5, Claude Fable 5.1, GPT-6 Astra and Gemini 4 Argon are proprietary and available only through cloud APIs, with no weights to self-host. For on-premises deployment, businesses run open-weight models instead. Faraday Machines ships DeepSeek V4.1 Flash, GLM-5.3-Flash, Qwen3.8-27B, MiMo-V2.6-Flash and MiniMax M3 on its standard tiers, with GLM-5.3 and Kimi K3 on larger setups.
Which open-source AI model fits on a single Mac Studio?
Of the models compared here, Qwen3.8-27B is the one that fits a single 128GB Mac Studio at 4-bit. It is a dense 27B vision-language model released under Apache 2.0, needs roughly 17 GB at 4-bit quantization and handles a 262K-token context. The Faraday Machines Starter tier, one Mac Studio M5 Max for $299 US a month, runs it; larger models such as GLM-5.3-Flash need a bigger Mac Studio, the 512GB Scale tier, at 4-bit. Unsloth’s 1- to 2-bit build of GLM-5.3-Flash (about 93–109 GB) fits the 256GB Growth tier, though lower-bit builds trade some quality, most noticeably on long or precise work.
Do all open-weight AI models have the same license?
No. DeepSeek V4.1 Flash, GLM-5.3-Flash and MiMo-V2.6 ship under the MIT license, and Qwen3.8-27B under Apache 2.0, all permissive for commercial use. GLM-5.3 moved from MIT to a bespoke license that requires Z.ai’s security review for model-as-a-service businesses above $10B in annual revenue, and Kimi K3 uses a custom license with thresholds for very large commercial deployments.
Detailed Model Profiles
Kimi K3
2.8T-parameter open-weight model for large clusters.
GLM-5.3
Z.ai’s latest. The MIT-licensed Flash version fits the Scale tier.
DeepSeek V4.1 Flash
552B MoE, MIT license, 1M context. Runs on Faraday’s Scale tier.
MiniMax M3
Multimodal, long-context agentic model for the Scale tier.
Qwen3.8
Qwen3.8-27B: Apache 2.0, vision-language, runs on a single Mac Studio.
MiMo-V2.6
Top open-weight model on Intelligence Index v4.3 (Pro). Flash fits the Scale tier.
Claude Opus 5.5
Top score on Intelligence Index v4.3. Cloud API only.
GPT-6 Astra
OpenAI’s flagship and the GPT-6 family prices. Cloud API only.
Previous generation: profiles for Kimi K2.6, GLM-5.1, Qwen 3.6, Claude Opus 4.7 and GPT-5.4 stay online for reference. Each has been superseded by the models above.